Abstract: UAVs have been widely used in the inspection tasks of power grid lines and electrical towers due to their advantages of flexibility, low cost and strong maneuverability. Because of the limited range of a single UAV, multiple UAVs are required to cooperate in a wide range grid inspection. However, the traditional planning methods cannot work well because of slow computing speed and unobvious collaborative effect. To remedy these deficits, a new mission planning algorithm is proposed in this work, which is based on multi-agent reinforcement learning algorithm QMIX. On the basis of the framework of intensive training and decentralized execution, this algorithm establishes RNN network for each UAV and gets the joint action value function guideline for training by mixing network. To simulate the collaboration capabilities of multi-agents, a reward function for collaboration task is designed, and it solves the problem of low collaboration efficiency in multi-UAV mission planning. The simulation results demonstrate that the proposed algorithm takes 350.4 seconds less than VDN algorithm.

Key words: reinforcement learning, power inspection, multi-agent collaboration